Journal of Spatial Analysis

Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables.    The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate.    It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation.     eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated.     The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030).    Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index.    The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas.    Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin  provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased.    Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.  
[1]. Expert Team on Climate Change Detection and Monitoring Indices

Accessibility to precise spatial and real time data plays a valuable role in the velocity and quality of flood relief operation and subsequently, scales the human and financial losses down. Flood real time data collection and processing, for instance, precise location and situation of flood victims may be a big challenge in Iran regarding the hardware facilities (such as high resolution aerial imagery devices) owned by the correspond organizations. To overcome the mentioned inabilities as well as reducing the financial costs for real time monitoring purpose of a flood, the current research intended to use the capacity of the flood victims and other volunteers to collect and upload real time data to rescue themselves. By means of this, flood real time spatial and non-spatial data collection is applicable via public and per-person participation based on the needs of each victims. The current Open Source workflow has been so designed that by using a browser like Mozilla, Explorer, Chrome and etc., and without the need for installing any software, the victim transmits his/her exact geographic location (captured automatically by the designed web service) and other multimedia data such as video-photo. Also, the flood-affected person announces the type of the damage and consequently, demanded rescue operation to the managers as a text information. After data processing on the server, the information is represented as a real time rescue map for decision making. The rescue plan may be mapped based on the singular aid as well as plural plan in the cluster form specialized for a particular group of victims in each bounding box. To design the web service, a client architecture for victims, other volunteers and managers has been developed, for implementing the service, Open Source technologies, server-side and client-side programming languages, Geoserver and WFS (Web Feature Service) standard adopted by OGC for spatially-enabled representation of victims demands, have been exploited. The research result is a browser-based service in which the client service offers automatic zooming to the current location of the clients and sends the rescue request including personal identifications and the type of injury using PHP (stands for Hypertext Preprocessor) and SQL (Structured Query Language). In the other side, on the client side designed for managers, the requested rescue submitted by the victims and other volunteers are mapped and displayed real time by OpenLayers and WFS. The result introduces an efficient applicable method for gathering real time and high accuracy geographic-multimedia-text data collection and consequently, extremely reduces the relief operation costs. Finally, the proposed methodology causes better performance and spatially clustering of victims to decrease the aftermath of the flood in a region like Iran suffers from the lack of expensive hardware technologies for precise data collection and transmission.